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DATA SCIENCE:
Q1. What is Data Science? List the differences between supervised
and unsupervised learning.
Data Science is a blend of various tools, algorithms, and machine learning
principles with the goal to discover hidden patterns from the raw data.
How is this different from what statisticians have been doing for years?
The answer lies in the difference between explaining and predicting.
Arpit Singh
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The differences between supervised and unsupervised learning are as
follows;
Supervised Learning
Unsupervised Learning
Input data is labelled.
Input data is unlabelled.
Uses a training data set.
Uses the input data set.
Used for prediction.
Used for analysis.
Enables classification and
regression.
Enables Classification, Density Estimation, &
Dimension Reduction
Q2. What is Selection Bias?
Selection bias is a kind of error that occurs when the researcher decides who is
going to be studied. It is usually associated with research where the selection
of participants isn‘t random. It is sometimes referred to as the selection effect.
It is the distortion of statistical analysis, resulting from the method of
collecting samples. If the selection bias is not taken into account, then some
conclusions of the study may not be accurate.
The types of selection bias include:
1. Sampling bias: It is a systematic error due to a non-random sample of
a population causing some members of the population to be less likely
to be included than others resulting in a biased sample.
2. Time interval: A trial may be terminated early at an extreme value
(often for ethical reasons), but the extreme value is likely to be reached
by the variable with the largest variance, even if all variables have a
similar mean.
3. Data: When specific subsets of data are chosen to support a conclusion
or rejection of bad data on arbitrary grounds, instead of according to
previously stated or generally agreed criteria.
4. Attrition: Attrition bias is a kind of selection bias caused by attrition
(loss of participants) discounting trial subjects/tests that did not run to
completion.
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Q3. What is bias-variance trade-off?
Bias: Bias is an error introduced in your model due to oversimplification of
the machine learning algorithm. It can lead to underfitting. When you train
your model at that time model makes simplified assumptions to make the
target function easier to understand.
Low bias machine learning algorithms — Decision Trees, k-NN and SVM
High bias machine learning algorithms — Linear Regression, Logistic
Regression
Variance: Variance is error introduced in your model due to complex
machine learning algorithm, your model learns noise also from the training
data set and performs badly on test data set. It can lead to high sensitivity and
overfitting.
Normally, as you increase the complexity of your model, you will see a
reduction in error due to lower bias in the model. However, this only happens
until a particular point. As you continue to make your model more complex,
you end up over-fitting your model and hence your model will start suffering
from high variance.
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Bias-Variance trade-off: The goal of any supervised machine learning
algorithm is to have low bias and low variance to achieve good prediction
performance.
1. The k-nearest neighbour algorithm has low bias and high variance, but
the trade-off can be changed by increasing the value of k which
increases the number of neighbours that contribute to the prediction
and in turn increases the bias of the model.
2. The support vector machine algorithm has low bias and high variance,
but the trade-off can be changed by increasing the C parameter that
influences the number of violations of the margin allowed in the
training data which increases the bias but decreases the variance.
There is no escaping the relationship between bias and variance in machine
learning. Increasing the bias will decrease the variance. Increasing the
variance will decrease bias.
Q4. What is a confusion matrix?
The confusion matrix is a 2X2 table that contains 4 outputs provided by
the binary classifier. Various measures, such as error-rate, accuracy,
specificity, sensitivity, precision and recall are derived from it.
Confusion Matrix
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A data set used for performance evaluation is called a test data set. It should
contain the correct labels and predicted labels.
The predicted labels will exactly the same if the performance of a binary
classifier is perfect.
The predicted labels usually match with part of the observed labels in real-
world scenarios.
A binary classifier predicts all data instances of a test data set as either
positive or negative. This produces four outcomes-
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1. True-positive(TP) — Correct positive prediction
2. False-positive(FP) — Incorrect positive prediction
3. True-negative(TN) — Correct negative prediction
4. False-negative(FN) — Incorrect negative prediction
Basic measures derived from the confusion matrix
1. Error Rate = (FP+FN)/(P+N)
2. Accuracy = (TP+TN)/(P+N)
3. Sensitivity(Recall or True positive rate) = TP/P
4. Specificity(True negative rate) = TN/N
5. Precision(Positive predicted value) = TP/(TP+FP)
6. F-Score(Harmonic mean of precision and recall) =
(1+b)(PREC.REC)/(b²PREC+REC) where b is commonly 0.5, 1, 2.
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STATISTICS:
Q5. What is the difference between “long” and “wide” format data?
In the wide-format, a subject‘s repeated responses will be in a single row,
and each response is in a separate column. In the long-format, each row is a
one-time point per subject. You can recognize data in wide format by the fact
that columns generally represent groups.
Q6. What do you understand by the term Normal Distribution?
Data is usually distributed in different ways with a bias to the left or to the
right or it can all be jumbled up.
However, there are chances that data is distributed around a central value
without any bias to the left or right and reaches normal distribution in the
form of a bell-shaped curve.
Figure: Normal distribution in a bell curve
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The random variables are distributed in the form of a symmetrical, bell-
shaped curve.
Properties of Normal Distribution are as follows;
1. Unimodal -one mode
2. Symmetrical -left and right halves are mirror images
3. Bell-shaped -maximum height (mode) at the mean
4. Mean, Mode, and Median are all located in the center
5. Asymptotic
Q7. What is correlation and covariance in statistics?
Covariance and Correlation are two mathematical concepts; these two
approaches are widely used in statistics. Both Correlation and Covariance
establish the relationship and also measure the dependency between two
random variables. Though the work is similar between these two in
mathematical terms, they are different from each other.
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Correlation: Correlation is considered or described as the best technique for
measuring and also for estimating the quantitative relationship between two
variables. Correlation measures how strongly two variables are related.
Covariance: In covariance two items vary together and it‘s a measure that
indicates the extent to which two random variables change in cycle. It is a
statistical term; it explains the systematic relation between a pair of random
variables, wherein changes in one variable reciprocal by a corresponding
change in another variable.
Q8. What is the difference between Point Estimates and Confidence
Interval?
Point Estimation gives us a particular value as an estimate of a population
parameter. Method of Moments and Maximum Likelihood estimator methods
are used to derive Point Estimators for population parameters.
A confidence interval gives us a range of values which is likely to contain the
population parameter. The confidence interval is generally preferred, as it tells
us how likely this interval is to contain the population parameter. This
likeliness or probability is called Confidence Level or Confidence coefficient
and represented by 1 — alpha, where alpha is the level of significance.
Q9. What is the goal of A/B Testing?
It is a hypothesis testing for a randomized experiment with two variables A
and B.
The goal of A/B Testing is to identify any changes to the web page to maximize
or increase the outcome of interest. A/B testing is a fantastic method for
figuring out the best online promotional and marketing strategies for your
business. It can be used to test everything from website copy to sales emails to
search ads
An example of this could be identifying the click-through rate for a banner ad.
Q10. What is p-value?
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When you perform a hypothesis test in statistics, a p-value can help you
determine the strength of your results. p-value is a number between 0 and 1.
Based on the value it will denote the strength of the results. The claim which is
on trial is called the Null Hypothesis.
Low p-value (≤ 0.05) indicates strength against the null hypothesis which
means we can reject the null Hypothesis. High p-value (≥ 0.05) indicates
strength for the null hypothesis which means we can accept the null
Hypothesis p-value of 0.05 indicates the Hypothesis could go either way. To
put it in another way,
High P values: your data are likely with a true null. Low P values: your data
are unlikely with a true null.
Q11. In any 15-minute interval, there is a 20% probability that you
will see at least one shooting star. What is the probability that you
see at least one shooting star in the period of an hour?
Probability of not seeing any shooting star in 15 minutes is
= 1 – P(Seeing one shooting star )
= 1 – 0.2 = 0.8
Probability of not seeing any shooting star in the period of one hour
= (0.8) ^ 4 = 0.4096
Probability of seeing at least one shooting star in the one hour
= 1 – P( Not seeing any star )
= 1 – 0.4096 = 0.5904
Q12. How can you generate a random number between 1 – 7 with
only a die?
Any die has six sides from 1-6. There is no way to get seven equal
outcomes from a single rolling of a die. If we roll the die twice and
consider the event of two rolls, we now have 36 different outcomes.
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To get our 7 equal outcomes we have to reduce this 36 to a number
divisible by 7. We can thus consider only 35 outcomes and exclude the
other one.
A simple scenario can be to exclude the combination (6,6), i.e., to roll
the die again if 6 appears twice.
All the remaining combinations from (1,1) till (6,5) can be divided into 7
parts of 5 each. This way all the seven sets of outcomes are equally
likely.
Q13. A certain couple tells you that they have two children, at least
one of which is a girl. What is the probability that they have two
girls?
In the case of two children, there are 4 equally likely possibilities
BB, BG, GB and GG;
where B = Boy and G = Girl and the first letter denotes the first child.
From the question, we can exclude the first case of BB. Thus from the
remaining 3 possibilities of BG, GB & BB, we have to find the probability of
the case with two girls.
Thus, P(Having two girls given one girl) = 1 / 3
Q14. A jar has 1000 coins, of which 999 are fair and 1 is double
headed. Pick a coin at random, and toss it 10 times. Given that you
see 10 heads, what is the probability that the next toss of that coin
is also a head?
There are two ways of choosing the coin. One is to pick a fair coin and the
other is to pick the one with two heads.
Probability of selecting fair coin = 999/1000 = 0.999
Probability of selecting unfair coin = 1/1000 = 0.001
Selecting 10 heads in a row = Selecting fair coin * Getting 10
heads + Selecting an unfair coin
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P (A) = 0.999 * (1/2)^5 = 0.999 * (1/1024) = 0.000976
P (B) = 0.001 * 1 = 0.001
P( A / A + B ) = 0.000976 / (0.000976 + 0.001) = 0.4939
P( B / A + B ) = 0.001 / 0.001976 = 0.5061
Probability of selecting another head = P(A/A+B) * 0.5 + P(B/A+B) * 1
= 0.4939 * 0.5 + 0.5061 = 0.7531
Q15. What do you understand by statistical power of sensitivity and
how do you calculate it?
Sensitivity is commonly used to validate the accuracy of a classifier (Logistic,
SVM, Random Forest etc.).
Sensitivity is nothing but ―Predicted True events/ Total events‖. True events
here are the events which were true and model also predicted them as true.
Calculation of seasonality is pretty straightforward.
Seasonality = ( True Positives ) / ( Positives in Actual Dependent
Variable )
Q16. Why Is Re-sampling Done?
Resampling is done in any of these cases:
Estimating the accuracy of sample statistics by using subsets of
accessible data or drawing randomly with replacement from a set of
data points
Substituting labels on data points when performing significance tests
Validating models by using random subsets (bootstrapping, cross-
validation)
Q17. What are the differences between over-fitting and under-
fitting?
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In statistics and machine learning, one of the most common tasks is to fit
a model to a set of training data, so as to be able to make reliable predictions
on general untrained data.
In overfitting, a statistical model describes random error or noise instead of
the underlying relationship. Overfitting occurs when a model is excessively
complex, such as having too many parameters relative to the number of
observations. A model that has been overfitted, has poor predictive
performance, as it overreacts to minor fluctuations in the training data.
Underfitting occurs when a statistical model or machine learning algorithm
cannot capture the underlying trend of the data. Underfitting would occur, for
example, when fitting a linear model to non-linear data. Such a model too
would have poor predictive performance.
Q18. How to combat Overfitting and Underfitting?
To combat overfitting and underfitting, you can resample the data to estimate
the model accuracy (k-fold cross-validation) and by having a validation
dataset to evaluate the model.
Q19. What is regularisation? Why is it useful?
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Regularisation is the process of adding tuning parameter to a model to induce
smoothness in order to prevent overfitting. This is most often done by adding
a constant multiple to an existing weight vector. This constant is often the
L1(Lasso) or L2(ridge). The model predictions should then minimize the loss
function calculated on the regularized training set.
Q20. What Is the Law of Large Numbers?
It is a theorem that describes the result of performing the same experiment a
large number of times. This theorem forms the basis of frequency-
style thinking. It says that the sample means, the sample variance and the
sample standard deviation converge to what they are trying to estimate.
Q21. What Are Confounding Variables?
In statistics, a confounder is a variable that influences both the dependent
variable and independent variable.
For example, if you are researching whether a lack of exercise leads to weight
gain,
lack of exercise = independent variable
weight gain = dependent variable.
A confounding variable here would be any other variable that affects both of
these variables, such as the age of the subject.
Q22. What Are the Types of Biases That Can Occur During
Sampling?
Selection bias
Under coverage bias
Survivorship bias
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Q23. What is Survivorship Bias?
It is the logical error of focusing aspects that support surviving some process
and casually overlooking those that did not work because of their lack of
prominence. This can lead to wrong conclusions in numerous different means.
Q24. What is selection Bias?
Selection bias occurs when the sample obtained is not representative of the
population intended to be analysed.
Q25. Explain how a ROC curve works?
The ROC curve is a graphical representation of the contrast between true
positive rates and false-positive rates at various thresholds. It is often used as
a proxy for the trade-off between the sensitivity (true positive rate) and false-
positive rate.
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Q26. What is TF/IDF vectorization?
TF–IDF is short for term frequency-inverse document frequency, is a
numerical statistic that is intended to reflect how important a word is to a
document in a collection or corpus. It is often used as a weighting factor in
information retrieval and text mining.
The TF–IDF value increases proportionally to the number of times a word
appears in the document but is offset by the frequency of the word in the
corpus, which helps to adjust for the fact that some words appear more
frequently in general.
Q27. Why we generally use Softmax non-linearity function as last
operation in-network?
It is because it takes in a vector of real numbers and returns a probability
distribution. Its definition is as follows. Let x be a vector of real numbers
(positive, negative, whatever, there are no constraints).
Then the i‘th component of Softmax(x) is —
It should be clear that the output is a probability distribution: each element is
non-negative and the sum over all components is 1.
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DATA ANALYSIS:
Q28. Python or R – Which one would you prefer for text analytics?
We will prefer Python because of the following reasons:
Python would be the best option because it has Pandas library that
provides easy to use data structures and high-performance data analysis
tools.
R is more suitable for machine learning than just text analysis.
Python performs faster for all types of text analytics.
Q29. How does data cleaning plays a vital role in the analysis?
Data cleaning can help in analysis because:
Cleaning data from multiple sources helps to transform it into a format
that data analysts or data scientists can work with.
Data Cleaning helps to increase the accuracy of the model in machine
learning.
It is a cumbersome process because as the number of data sources
increases, the time taken to clean the data increases exponentially due
to the number of sources and the volume of data generated by these
sources.
It might take up to 80% of the time for just cleaning data making it a
critical part of the analysis task.
Q30. Differentiate between univariate, bivariate and multivariate
analysis.
Univariate analyses are descriptive statistical analysis techniques which can
be differentiated based on the number of variables involved at a given point of
time. For example, the pie charts of sales based on territory involve only one
variable and can the analysis can be referred to as univariate analysis.
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The bivariate analysis attempts to understand the difference between two
variables at a time as in a scatterplot. For example, analyzing the volume of
sale and spending can be considered as an example of bivariate analysis.
Multivariate analysis deals with the study of more than two variables to
understand the effect of variables on the responses.
Q31. Explain Star Schema.
It is a traditional database schema with a central table. Satellite tables map
IDs to physical names or descriptions and can be connected to the central fact
table using the ID fields; these tables are known as lookup tables and are
principally useful in real-time applications, as they save a lot of memory.
Sometimes star schemas involve several layers of summarization to recover
information faster.
Q32. What is Cluster Sampling?
Cluster sampling is a technique used when it becomes difficult to study the
target population spread across a wide area and simple random sampling
cannot be applied. Cluster Sample is a probability sample where each
sampling unit is a collection or cluster of elements.
For eg., A researcher wants to survey the academic performance of high school
students in Japan. He can divide the entire population of Japan into different
clusters (cities). Then the researcher selects a number of clusters depending
on his research through simple or systematic random sampling.
Let‘s continue our Data Science Interview Questions blog with some more
statistics questions.
Q33. What is Systematic Sampling?
Systematic sampling is a statistical technique where elements are selected
from an ordered sampling frame. In systematic sampling, the list is
progressed in a circular manner so once you reach the end of the list, it is
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progressed from the top again. The best example of systematic sampling is
equal probability method.
Q34. What are Eigenvectors and Eigenvalues?
Eigenvectors are used for understanding linear transformations. In data
analysis, we usually calculate the eigenvectors for a correlation or covariance
matrix. Eigenvectors are the directions along which a particular linear
transformation acts by flipping, compressing or stretching.
Eigenvalue can be referred to as the strength of the transformation in the
direction of eigenvector or the factor by which the compression occurs.
Q35. Can you cite some examples where a false positive is
important than a false negative?
Let us first understand what false positives and false negatives are.
False Positives are the cases where you wrongly classified a non-event
as an event a.k.a Type I error.
False Negatives are the cases where you wrongly classify events as
non-events, a.k.a Type II error.
Example 1: In the medical field, assume you have to give chemotherapy to
patients. Assume a patient comes to that hospital and he is tested positive for
cancer, based on the lab prediction but he actually doesn‘t have cancer. This is
a case of false positive. Here it is of utmost danger to start chemotherapy on
this patient when he actually does not have cancer. In the absence of
cancerous cell, chemotherapy will do certain damage to his normal healthy
cells and might lead to severe diseases, even cancer.
Example 2: Let‘s say an e-commerce company decided to give $1000 Gift
voucher to the customers whom they assume to purchase at least $10,000
worth of items. They send free voucher mail directly to 100 customers without
any minimum purchase condition because they assume to make at least 20%
profit on sold items above $10,000. Now the issue is if we send the $1000 gift
vouchers to customers who have not actually purchased anything but are
marked as having made $10,000 worth of purchase.
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Q36. Can you cite some examples where a false negative important
than a false positive?
Example 1: Assume there is an airport ‗A‘ which has received high-security
threats and based on certain characteristics they identify whether a particular
passenger can be a threat or not. Due to a shortage of staff, they decide to scan
passengers being predicted as risk positives by their predictive model. What
will happen if a true threat customer is being flagged as non-threat by airport
model?
Example 2: What if Jury or judge decides to make a criminal go free?
Example 3: What if you rejected to marry a very good person based on your
predictive model and you happen to meet him/her after a few years and
realize that you had a false negative?
Q37. Can you cite some examples where both false positive and
false negatives are equally important?
In the Banking industry giving loans is the primary source of making money
but at the same time if your repayment rate is not good you will not make any
profit, rather you will risk huge losses.
Banks don‘t want to lose good customers and at the same point in time, they
don‘t want to acquire bad customers. In this scenario, both the false positives
and false negatives become very important to measure.
Q38. Can you explain the difference between a Validation Set and a
Test Set?
A Validation set can be considered as a part of the training set as it is used
for parameter selection and to avoid overfitting of the model being built.
On the other hand, a Test Set is used for testing or evaluating the
performance of a trained machine learning model.
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In simple terms, the differences can be summarized as; training set is to fit the
parameters i.e. weights and test set is to assess the performance of the model
i.e. evaluating the predictive power and generalization.
Q39. Explain cross-validation.
Cross-validation is a model validation technique for evaluating how the
outcomes of statistical analysis will generalize to an independent
dataset. Mainly used in backgrounds where the objective is forecast and one
wants to estimate how accurately a model will accomplish in practice.
The goal of cross-validation is to term a data set to test the model in the
training phase (i.e. validation data set) in order to limit problems like
overfitting and get an insight on how the model will generalize to an
independent data set.
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MACHINE LEARNING:
Q40. What is Machine Learning?
Machine Learning explores the study and construction of algorithms that
can learn from and make predictions on data. Closely related to computational
statistics. Used to devise complex models and algorithms that lend themselves
to a prediction which in commercial use is known as predictive analytics.
Given below, is an image representing the various domains Machine Learning
lends itself to.
Q41. What is Supervised Learning?
Supervised learning is the machine learning task of inferring a function
from labeled training data. The training data consist of a set of training
examples.
Algorithms: Support Vector Machines, Regression, Naive Bayes, Decision
Trees, K-nearest Neighbor Algorithm and Neural Networks
E.g. If you built a fruit classifier, the labels will be “this is an orange, this is
an apple and this is a banana”, based on showing the classifier examples of
apples, oranges and bananas.
Q42. What is Unsupervised learning?
Unsupervised learning is a type of machine learning algorithm used to
draw inferences from datasets consisting of input data without labelled
responses.
Algorithms: Clustering, Anomaly Detection, Neural Networks and Latent
Variable Models
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E.g. In the same example, a fruit clustering will categorize as “fruits with soft
skin and lots of dimples”, “fruits with shiny hard skin” and “elongated yellow
fruits”.
Q43. What are the various classification algorithms?
The diagram lists the most important classification algorithms.
Q44. What is „Naive‟ in a Naive Bayes?
The Naive Bayes Algorithm is based on the Bayes Theorem. Bayes‘
theorem describes the probability of an event, based on prior knowledge of
conditions that might be related to the event.
The Algorithm is ‗naive‘ because it makes assumptions that may or may not
turn out to be correct.
Q45. Explain SVM algorithm in detail.
SVM stands for support vector machine, it is a supervised machine learning
algorithm which can be used for both Regression and Classification. If
you have n features in your training data set, SVM tries to plot it in n-
dimensional space with the value of each feature being the value of a
particular coordinate. SVM uses hyperplanes to separate out different classes
based on the provided kernel function.
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Q46. What are the support vectors in SVM?
In the diagram, we see that the thinner lines mark the distance from the
classifier to the closest data points called the support vectors (darkened data
points). The distance between the two thin lines is called the margin.
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Q47. What are the different kernels in SVM?
There are four types of kernels in SVM.
1. Linear Kernel
2. Polynomial kernel
3. Radial basis kernel
4. Sigmoid kernel
Q48. Explain Decision Tree algorithm in detail.
A decision tree is a supervised machine learning algorithm mainly used
for Regression and Classification. It breaks down a data set into smaller
and smaller subsets while at the same time an associated decision tree is
incrementally developed. The final result is a tree with decision nodes and leaf
nodes. A decision tree can handle both categorical and numerical data.
Q49. What are Entropy and Information gain in Decision tree
algorithm?
The core algorithm for building a decision tree is
called ID3. ID3 uses Entropy and Information Gain to construct a
decision tree.
Entropy
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A decision tree is built top-down from a root node and involve partitioning of
data into homogeneous subsets. ID3 uses enteropy to check the homogeneity
of a sample. If the sample is completely homogenious then entropy is zero and
if the sample is an equally divided it has entropy of one.
Information Gain
The Information Gain is based on the decrease in entropy after a dataset is
split on an attribute. Constructing a decision tree is all about finding attributes
that return the highest information gain.
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Q50. What is pruning in Decision Tree?
Pruning is a technique in machine learning and search algorithms that
reduces the size of decision trees by removing sections of the tree that
provide little power to classify instances. So, when we remove sub-nodes of a
decision node, this process is called pruning or opposite process of splitting.
Q51. What is logistic regression? State an example when you have
used logistic regression recently.
Logistic Regression often referred to as the logit model is a technique to
predict the binary outcome from a linear combination of predictor variables.
For example, if you want to predict whether a particular political leader will
win the election or not. In this case, the outcome of prediction is binary i.e. 0
or 1 (Win/Lose). The predictor variables here would be the amount of money
spent for election campaigning of a particular candidate, the amount of time
spent in campaigning, etc.
Q52. What is Linear Regression?
Linear regression is a statistical technique where the score of a variable Y is
predicted from the score of a second variable X. X is referred to as the
predictor variable and Y as the criterion variable.
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Q53. What Are the Drawbacks of the Linear Model?
Some drawbacks of the linear model are:
The assumption of linearity of the errors.
It can‘t be used for count outcomes or binary outcomes
There are overfitting problems that it can‘t solve
Q54. What is the difference between Regression and classification
ML techniques?
Both Regression and classification machine learning techniques come
under Supervised machine learning algorithms. In Supervised machine
learning algorithm, we have to train the model using labelled data set, While
training we have to explicitly provide the correct labels and algorithm tries to
learn the pattern from input to output. If our labels are discrete values then it
will a classification problem, e.g A,B etc. but if our labels are continuous
values then it will be a regression problem, e.g 1.23, 1.333 etc.
Q55. What are Recommender Systems?
Recommender Systems are a subclass of information filtering systems that
are meant to predict the preferences or ratings that a user would give to a
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product. Recommender systems are widely used in movies, news, research
articles, products, social tags, music, etc.
Examples include movie recommenders in IMDB, Netflix & BookMyShow,
product recommenders in e-commerce sites like Amazon, eBay & Flipkart,
YouTube video recommendations and game recommendations in Xbox.
Q56. What is Collaborative filtering?
The process of filtering used by most of the recommender systems to find
patterns or information by collaborating viewpoints, various data sources and
multiple agents.
An example of collaborative filtering can be to predict the rating of a particular
user based on his/her ratings for other movies and others‘ ratings for all
movies. This concept is widely used in recommending movies in IMDB,
Netflix & BookMyShow, product recommenders in e-commerce sites like
Amazon, eBay & Flipkart, YouTube video recommendations and game
recommendations in Xbox.
Q57. How can outlier values be treated?
Outlier values can be identified by using univariate or any other graphical
analysis method. If the number of outlier values is few then they can be
assessed individually but for a large number of outliers, the values can be
substituted with either the 99th or the 1st percentile values.
All extreme values are not outlier values. The most common ways to treat
outlier values
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1. To change the value and bring it within a range.
2. To just remove the value.
Q58. What are the various steps involved in an analytics project?
The following are the various steps involved in an analytics project:
1. Understand the Business problem
2. Explore the data and become familiar with it.
3. Prepare the data for modelling by detecting outliers, treating missing
values, transforming variables, etc.
4. After data preparation, start running the model, analyze the result and
tweak the approach. This is an iterative step until the best possible
outcome is achieved.
5. Validate the model using a new data set.
6. Start implementing the model and track the result to analyze the
performance of the model over the period of time.
Q59. During analysis, how do you treat missing values?
The extent of the missing values is identified after identifying the variables
with missing values. If any patterns are identified the analyst has to
concentrate on them as it could lead to interesting and meaningful business
insights.
If there are no patterns identified, then the missing values can be substituted
with mean or median values (imputation) or they can simply be
ignored. Assigning a default value which can be mean, minimum or maximum
value. Getting into the data is important.
If it is a categorical variable, the default value is assigned. The missing value is
assigned a default value. If you have a distribution of data coming, for normal
distribution give the mean value.
If 80% of the values for a variable are missing then you can answer that you
would be dropping the variable instead of treating the missing values.
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Q60. How will you define the number of clusters in a clustering
algorithm?
Though the Clustering Algorithm is not specified, this question is mostly in
reference to K-Means clustering where ―K‖ defines the number of
clusters. The objective of clustering is to group similar entities in a way that
the entities within a group are similar to each other but the groups are
different from each other.
For example, the following image shows three different groups.
Within Sum of squares is generally used to explain the homogeneity within a
cluster. If you plot WSS for a range of number of clusters, you will get the plot
shown below.
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The Graph is generally known as Elbow Curve.
Red circled a point in above graph i.e. Number of Cluster =6 is the
point after which you don‘t see any decrement in WSS.
This point is known as the bending point and taken as K in K – Means.
This is the widely used approach but few data scientists also use Hierarchical
clustering first to create dendrograms and identify the distinct groups from
there.
Q61. What is Ensemble Learning?
Ensemble Learning is basically combining a diverse set of learners(Individual
models) together to improvise on the stability and predictive power of the
model.
Q62. Describe in brief any type of Ensemble Learning?
Ensemble learning has many types but two more popular ensemble learning
techniques are mentioned below.
Bagging
Bagging tries to implement similar learners on small sample populations and
then takes a mean of all the predictions. In generalised bagging, you can use
different learners on different population. As you expect this helps us to
reduce the variance error.
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Boosting
Boosting is an iterative technique which adjusts the weight of an observation
based on the last classification. If an observation was classified incorrectly, it
tries to increase the weight of this observation and vice versa. Boosting in
general decreases the bias error and builds strong predictive models.
However, they may over fit on the training data.
Q63. What is a Random Forest? How does it work?
Random forest is a versatile machine learning method capable of
performing both regression and classification tasks. It is also used for
dimensionality reduction, treats missing values, outlier values. It is a type of
ensemble learning method, where a group of weak models combine to form a
powerful model.
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In Random Forest, we grow multiple trees as opposed to a single tree. To
classify a new object based on attributes, each tree gives a classification. The
forest chooses the classification having the most votes(Overall the trees in the
forest) and in case of regression, it takes the average of outputs by different
trees.
Q64. How Do You Work Towards a Random Forest?
The underlying principle of this technique is that several weak learners
combined to provide a keen learner. The steps involved are
Build several decision trees on bootstrapped training samples of data
On each tree, each time a split is considered, a random sample of mm
predictors is chosen as split candidates, out of all pp predictors
Rule of thumb: At each split m=p√m=p
Predictions: At the majority rule
Q65. What cross-validation technique would you use on a time
series data set?
Instead of using k-fold cross-validation, you should be aware of the fact that a
time series is not randomly distributed data — It is inherently ordered by
chronological order.
In case of time series data, you should use techniques like forward=chaining
— Where you will be model on past data then look at forward-facing data.
fold 1: training[1], test[2]
fold 1: training[1 2], test[3]
fold 1: training[1 2 3], test[4]
fold 1: training[1 2 3 4], test[5]
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Q66. What is a Box-Cox Transformation?
The dependent variable for a regression analysis might not satisfy one or more
assumptions of an ordinary least squares regression. The residuals could
either curve as the prediction increases or follow the skewed distribution. In
such scenarios, it is necessary to transform the response variable so that the
data meets the required assumptions. A Box cox transformation is a statistical
technique to transform non-normal dependent variables into a normal shape.
If the given data is not normal then most of the statistical techniques assume
normality. Applying a box cox transformation means that you can run a
broader number of tests.
A Box-Cox transformation is a way to transform non-normal dependent
variables into a normal shape. Normality is an important assumption for
many statistical techniques, if your data isn‘t normal, applying a Box-Cox
means that you are able to run a broader number of tests. The Box-Cox
transformation is named after statisticians George Box and Sir David
Roxbee Cox who collaborated on a 1964 paper and developed the technique.
Q67. How Regularly Must an Algorithm be Updated?
You will want to update an algorithm when:
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You want the model to evolve as data streams through infrastructure
The underlying data source is changing
There is a case of non-stationarity
The algorithm underperforms/ results lack accuracy
Q68. If you are having 4GB RAM in your machine and you want to
train your model on 10GB data set. How would you go about this
problem? Have you ever faced this kind of problem in your
machine learning/data science experience so far?
First of all, you have to ask which ML model you want to train.
For Neural networks: Batch size with Numpy array will work.
Steps:
1. Load the whole data in the Numpy array. Numpy array has a property to
create a mapping of the complete data set, it doesn‘t load complete data
set in memory.
2. You can pass an index to Numpy array to get required data.
3. Use this data to pass to the Neural network.
4. Have a small batch size.
For SVM: Partial fit will work
Steps:
1. Divide one big data set in small size data sets.
2. Use a partial fit method of SVM, it requires a subset of the complete
data set.
3. Repeat step 2 for other subsets.
However, you could actually face such an issue in reality. So, you could check
out the best laptop for Machine Learning to prevent that. Having said
that, let‘s move on to some questions on deep learning.
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DEEP LEARNING:
Q69. What do you mean by Deep Learning?
Deep Learning is nothing but a paradigm of machine learning which has
shown incredible promise in recent years. This is because of the fact that Deep
Learning shows a great analogy with the functioning of the human brain.
Q70. What is the difference between machine learning and deep
learning?
Machine learning is a field of computer science that gives computers the
ability to learn without being explicitly programmed. Machine learning can be
categorised in the following three categories.
1. Supervised machine learning,
2. Unsupervised machine learning,
3. Reinforcement learning
Deep Learning is a subfield of machine learning concerned with algorithms
inspired by the structure and function of the brain called artificial neural
networks.
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Q71. What, in your opinion, is the reason for the popularity of Deep
Learning in recent times?
Now although Deep Learning has been around for many years, the major
breakthroughs from these techniques came just in recent years. This is
because of two main reasons:
The increase in the amount of data generated through various sources
The growth in hardware resources required to run these models
GPUs are multiple times faster and they help us build bigger and deeper deep
learning models in comparatively less time than we required previously.
Q72. What is reinforcement learning?
Reinforcement Learning is learning what to do and how to map situations
to actions. The end result is to maximise the numerical reward signal. The
learner is not told which action to take but instead must discover which action
will yield the maximum reward. Reinforcement learning is inspired by the
learning of human beings, it is based on the reward/penalty mechanism.
Q73. What are Artificial Neural Networks?
Artificial Neural networks are a specific set of algorithms that have
revolutionized machine learning. They are inspired by biological neural
networks. Neural Networks can adapt to changing the input so the network
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generates the best possible result without needing to redesign the output
criteria.
Q74. Describe the structure of Artificial Neural Networks?
Artificial Neural Networks works on the same principle as a biological Neural
Network. It consists of inputs which get processed with weighted sums and
Bias, with the help of Activation Functions.
Q75. How Are Weights Initialized in a Network?
There are two methods here: we can either initialize the weights to zero or
assign them randomly.
Initializing all weights to 0: This makes your model similar to a linear model.
All the neurons and every layer perform the same operation, giving the same
output and making the deep net useless.
Initializing all weights randomly: Here, the weights are assigned randomly by
initializing them very close to 0. It gives better accuracy to the model since
every neuron performs different computations. This is the most commonly
used method.
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Q76. What Is the Cost Function?
Also referred to as ―loss‖ or ―error,‖ cost function is a measure to evaluate how
good your model‘s performance is. It‘s used to compute the error of the output
layer during backpropagation. We push that error backwards through the
neural network and use that during the different training functions.
Q77. What Are Hyperparameters?
With neural networks, you‘re usually working with hyperparameters once
the data is formatted correctly. A hyperparameter is a parameter whose value
is set before the learning process begins. It determines how a network is
trained and the structure of the network (such as the number of hidden units,
the learning rate, epochs, etc.).
Q78. What Will Happen If the Learning Rate Is Set inaccurately
(Too Low or Too High)?
When your learning rate is too low, training of the model will progress very
slowly as we are making minimal updates to the weights. It will take many
updates before reaching the minimum point.
If the learning rate is set too high, this causes undesirable divergent behaviour
to the loss function due to drastic updates in weights. It may fail to converge
(model can give a good output) or even diverge (data is too chaotic for the
network to train).
Q79. What Is the Difference Between Epoch, Batch, and Iteration in
Deep Learning?
Epoch – Represents one iteration over the entire dataset (everything
put into the training model).
Batch – Refers to when we cannot pass the entire dataset into the
neural network at once, so we divide the dataset into several batches.
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Iteration – if we have 10,000 images as data and a batch size of 200.
then an epoch should run 50 iterations (10,000 divided by 50).
Q80. What Are the Different Layers on CNN?
There are four layers in CNN:
1. Convolutional Layer – the layer that performs a convolutional
operation, creating several smaller picture windows to go over the data.
2. ReLU Layer – it brings non-linearity to the network and converts all the
negative pixels to zero. The output is a rectified feature map.
3. Pooling Layer – pooling is a down-sampling operation that reduces the
dimensionality of the feature map.
4. Fully Connected Layer – this layer recognizes and classifies the objects
in the image.
Q81. What Is Pooling on CNN, and How Does It Work?
Pooling is used to reduce the spatial dimensions of a CNN. It performs down-
sampling operations to reduce the dimensionality and creates a pooled feature
map by sliding a filter matrix over the input matrix.
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Q82. What are Recurrent Neural Networks(RNNs)?
RNNs are a type of artificial neural networks designed to recognise the
pattern from the sequence of data such as Time series, stock market and
government agencies etc. To understand recurrent nets, first, you have to
understand the basics of feedforward nets.
Both these networks RNN and feed-forward named after the way they channel
information through a series of mathematical orations performed at the nodes
of the network. One feeds information through straight(never touching the
same node twice), while the other cycles it through a loop, and the latter are
called recurrent.
Recurrent networks, on the other hand, take as their input, not just the
current input example they see, but also the what they have perceived
previously in time.
The decision a recurrent neural network reached at time t-1 affects the
decision that it will reach one moment later at time t. So recurrent networks
have two sources of input, the present and the recent past, which combine to
determine how they respond to new data, much as we do in life.
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The error they generate will return via backpropagation and be used to adjust
their weights until error can‘t go any lower. Remember, the purpose of
recurrent nets is to accurately classify sequential input. We rely on the
backpropagation of error and gradient descent to do so.
Q83. How Does an LSTM Network Work?
Long-Short-Term Memory (LSTM) is a special kind of recurrent neural
network capable of learning long-term dependencies, remembering
information for long periods as its default behaviour. There are three steps in
an LSTM network:
Step 1: The network decides what to forget and what to remember.
Step 2: It selectively updates cell state values.
Step 3: The network decides what part of the current state makes it to
the output.
Q84. What Is a Multi-layer Perceptron(MLP)?
As in Neural Networks, MLPs have an input layer, a hidden layer, and an
output layer. It has the same structure as a single layer perceptron with one
or more hidden layers. A single layer perceptron can classify only linear
separable classes with binary output (0,1), but MLP can classify nonlinear
classes.
Except for the input layer, each node in the other layers uses a nonlinear
activation function. This means the input layers, the data coming in, and the
activation function is based upon all nodes and weights being added together,
producing the output. MLP uses a supervised learning method called
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―backpropagation.‖ In backpropagation, the neural network calculates the
error with the help of cost function. It propagates this error backward from
where it came (adjusts the weights to train the model more accurately).
Q85. Explain Gradient Descent.
To Understand Gradient Descent, Let‘s understand what is a Gradient first.
A gradient measures how much the output of a function changes if you
change the inputs a little bit. It simply measures the change in all weights with
regard to the change in error. You can also think of a gradient as the slope of a
function.
Gradient Descent can be thought of climbing down to the bottom of a
valley, instead of climbing up a hill. This is because it is a minimization
algorithm that minimizes a given function (Activation Function).
Q86. What is exploding gradients?
While training an RNN, if you see exponentially growing (very large)
error gradients which accumulate and result in very large updates to neural
network model weights during training, they‘re known as exploding gradients.
At an extreme, the values of weights can become so large as to overflow and
result in NaN values.
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This has the effect of your model is unstable and unable to learn from your
training data.
Q87. What is vanishing gradients?
While training an RNN, your slope can become either too small; this makes
the training difficult. When the slope is too small, the problem is known as a
Vanishing Gradient. It leads to long training times, poor performance, and low
accuracy.
Q88. What is Back Propagation and Explain it‟s Working.
Backpropagation is a training algorithm used for multilayer neural
network. In this method, we move the error from an end of the network to all
weights inside the network and thus allowing efficient computation of the
gradient.
It has the following steps:
Forward Propagation of Training Data
Derivatives are computed using output and target
Back Propagate for computing derivative of error wrt output activation
Using previously calculated derivatives for output
Update the Weights
Q89. What are the variants of Back Propagation?
Stochastic Gradient Descent: We use only a single training example
for calculation of gradient and update parameters.
Batch Gradient Descent: We calculate the gradient for the whole
dataset and perform the update at each iteration.
Mini-batch Gradient Descent: It‘s one of the most popular
optimization algorithms. It‘s a variant of Stochastic Gradient Descent
and here instead of single training example, mini-batch of samples is
used.
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Q90. What are the different Deep Learning Frameworks?
Pytorch
TensorFlow
Microsoft Cognitive Toolkit
Keras
Caffe
Chainer
Q91. What is the role of the Activation Function?
The Activation function is used to introduce non-linearity into the neural
network helping it to learn more complex function. Without which the neural
network would be only able to learn linear function which is a linear
combination of its input data. An activation function is a function in an
artificial neuron that delivers an output based on inputs.
Q92. Name a few Machine Learning libraries for various purposes.
Purpose
Libraries
Scientific Computation
Numpy
Tabular Data
Pandas
Data Modelling & Preprocessing
Scikit Learn
Time-Series Analysis
Statsmodels
Text processing
Regular Expressions, NLTK
Deep Learning
Tensorflow, Pytorch
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Q93. What is an Auto-Encoder?
Auto-encoders are simple learning networks that aim to transform inputs
into outputs with the minimum possible error. This means that we want the
output to be as close to input as possible. We add a couple of layers between
the input and the output, and the sizes of these layers are smaller than the
input layer. The auto-encoder receives unlabelled input which is then encoded
to reconstruct the input.
Q94. What is a Boltzmann Machine?
Boltzmann machines have a simple learning algorithm that allows them to
discover interesting features that represent complex regularities in the
training data. The Boltzmann machine is basically used to optimise the
weights and the quantity for the given problem. The learning algorithm is very
slow in networks with many layers of feature detectors. ―Restricted
Boltzmann Machines‖ algorithm has a single layer of feature detectors
which makes it faster than the rest.
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Q95. What Is Dropout and Batch Normalization?
Dropout is a technique of dropping out hidden and visible units of a network
randomly to prevent overfitting of data (typically dropping 20 per cent of the
nodes). It doubles the number of iterations needed to converge the network.
Batch normalization is the technique to improve the performance and stability
of neural networks by normalizing the inputs in every layer so that they have
mean output activation of zero and standard deviation of one.
Q96. What Is the Difference Between Batch Gradient Descent and
Stochastic Gradient Descent?
Batch Gradient Descent
Stochastic Gradient Descent
The batch gradient computes the
gradient using the entire dataset.
The stochastic gradient computes the
gradient using a single sample.
It takes time to converge because the
volume of data is huge, and weights
update slowly.
It converges much faster than the
batch gradient because it updates
weight more frequently.
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Q97. Why Is Tensorflow the Most Preferred Library in Deep
Learning?
Tensorflow provides both C++ and Python APIs, making it easier to work on
and has a faster compilation time compared to other Deep Learning libraries
like Keras and Torch. Tensorflow supports both CPU and GPU computing
devices.
Q98. What Do You Mean by Tensor in Tensorflow?
A tensor is a mathematical object represented as arrays of higher dimensions.
These arrays of data with different dimensions and ranks fed as input to the
neural network are called ―Tensors.‖
Q99. What is the Computational Graph?
Everything in a tensorflow is based on creating a computational graph. It has a
network of nodes where each node operates, Nodes represent mathematical
operations, and edges represent tensors. Since data flows in the form of a
graph, it is also called a ―DataFlow Graph.‖
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Q100. What is a Generative Adversarial Network?
Suppose there is a wine shop purchasing wine from dealers, which they resell
later. But some dealers sell fake wine. In this case, the shop owner should be
able to distinguish between fake and authentic wine.
The forger will try different techniques to sell fake wine and make sure specific
techniques go past the shop owner‘s check. The shop owner would probably
get some feedback from wine experts that some of the wine is not original. The
owner would have to improve how he determines whether a wine is fake or
authentic.
The forger‘s goal is to create wines that are indistinguishable from the
authentic ones while the shop owner intends to tell if the wine is real or not
accurately
Let us understand this example with the help of an image.
There is a noise vector coming into the forger who is generating fake wine.
Here the forger acts as a Generator.
The shop owner acts as a Discriminator.
The Discriminator gets two inputs; one is the fake wine, while the other is the
real authentic wine. The shop owner has to figure out whether it is real or fake.
So, there are two primary components of Generative Adversarial Network
(GAN) named:
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1. Generator
2. Discriminator
The generator is a CNN that keeps keys producing images and is closer in
appearance to the real images while the discriminator tries to determine the
difference between real and fake images The ultimate aim is to make the
discriminator learn to identify real and fake images.
Apart from the very technical questions, your interviewer could
even hit you up with a few simple ones to check your overall
confidence, in the likes of the following.
Q101. What are the important skills to have in Python with regard
to data analysis?
The following are some of the important skills to possess which will come
handy when performing data analysis using Python.
Good understanding of the built-in data types especially lists,
dictionaries, tuples, and sets.
Mastery of N-dimensional NumPy Arrays.
Mastery of Pandas dataframes.
Ability to perform element-wise vector and matrix operations on
NumPy arrays.
Knowing that you should use the Anaconda distribution and the conda
package manager.
Familiarity with Scikit-learn. **Scikit-Learn Cheat Sheet**
Ability to write efficient list comprehensions instead of traditional for
loops.
Ability to write small, clean functions (important for any developer),
preferably pure functions that don‘t alter objects.
Knowing how to profile the performance of a Python script and how to
optimize bottlenecks.
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Q102. What are the differences between supervised and
unsupervised learning?
Supervised Learning
Unsupervised Learning
Uses known and labeled
data as input
Supervised learning has a
feedback mechanism
Most commonly used
supervised learning
algorithms are decision
trees, logistic regression,
and support vector
machine
Uses unlabeled data as
input
Unsupervised learning has
no feedback mechanism
Most commonly used
unsupervised learning
algorithms are k-means
clustering, hierarchical
clustering, and apriori
algorithm
Q103. How is logistic regression done?
Logistic regression measures the relationship between the dependent variable
(our label of what we want to predict) and one or more independent variables
(our features) by estimating probability using its underlying logistic function
(sigmoid).
The image shown below depicts how logistic regression works:
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The formula and graph for the sigmoid function are as shown:
Q104. Explain the steps in making a decision tree.
1. Take the entire data set as input
2. Calculate entropy of the target variable, as well as the predictor attributes
3. Calculate your information gain of all attributes (we gain information on
sorting different objects from each other)
4. Choose the attribute with the highest information gain as the root node
5. Repeat the same procedure on every branch until the decision node of each
branch is finalized
For example, let's say you want to build a decision tree to decide whether you
should accept or decline a job offer. The decision tree for this case is as shown:
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It is clear from the decision tree that an offer is accepted if:
Salary is greater than $50,000
The commute is less than an hour
Incentives are offered
Q105. How do you build a random forest model?
A random forest is built up of a number of decision trees. If you split the data
into different packages and make a decision tree in each of the different
groups of data, the random forest brings all those trees together.
Steps to build a random forest model:
1. Randomly select 'k' features from a total of 'm' features where k << m
2. Among the 'k' features, calculate the node D using the best split point
3. Split the node into daughter nodes using the best split
4. Repeat steps two and three until leaf nodes are finalized
5. Build forest by repeating steps one to four for 'n' times to create 'n' number
of trees
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Q106. How can you avoid the overfitting your model?
Overfitting refers to a model that is only set for a very small amount of data
and ignores the bigger picture. There are three main methods to avoid
overfitting:
1. Keep the model simple—take fewer variables into account, thereby
removing some of the noise in the training data
2. Use cross-validation techniques, such as k folds cross-validation
3. Use regularization techniques, such as LASSO, that penalize certain model
parameters if they're likely to cause overfitting
Q107. Differentiate between univariate, bivariate, and multivariate
analysis.
Univariate
Univariate data contains only one variable. The purpose of the univariate
analysis is to describe the data and find patterns that exist within it.
Example: height of students
Height (in cm)
164
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167.3
170
174.2
178
180
The patterns can be studied by drawing conclusions using mean, median,
mode, dispersion or range, minimum, maximum, etc.
Bivariate
Bivariate data involves two different variables. The analysis of this type of data
deals with causes and relationships and the analysis is done to determine the
relationship between the two variables.
Example: temperature and ice cream sales in the summer season
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Temperature (in Celsius)
Sales
20
2,000
25
2,100
26
2,300
28
2,400
30
2,600
36
3,100
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Here, the relationship is visible from the table that temperature and sales are
directly proportional to each other. The hotter the temperature, the better the
sales.
Multivariate
Multivariate data involves three or more variables, it is categorized under
multivariate. It is similar to a bivariate but contains more than one dependent
variable.
Example: data for house price prediction
No. of
rooms
Floors
Area (sq ft)
Price
2
0
900
$400,000
3
2
1,100
$600,000
3.5
5
1,500
$900,000
4
3
2,100
$1,200,000
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The patterns can be studied by drawing conclusions using mean, median, and
mode, dispersion or range, minimum, maximum, etc. You can start describing
the data and using it to guess what the price of the house will be.
Q108. What are the feature selection methods used to select the
right variables?
There are two main methods for feature selection:
Filter Methods
This involves:
Linear discrimination analysis
ANOVA
Chi-Square
The best analogy for selecting features is "bad data in, bad answer out." When
we're limiting or selecting the features, it's all about cleaning up the data
coming in.
Wrapper Methods
This involves:
Forward Selection: We test one feature at a time and keep adding them
until we get a good fit
Backward Selection: We test all the features and start removing them to see
what works better
Recursive Feature Elimination: Recursively looks through all the different
features and how they pair together
Wrapper methods are very labor-intensive, and high-end computers are
needed if a lot of data analysis is performed with the wrapper method.
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Q109. In your choice of language, write a program that prints the
numbers ranging from one to 50.
But for multiples of three, print "Fizz" instead of the number and for the
multiples of five, print "Buzz." For numbers which are multiples of both three
and five, print "FizzBuzz"
The code is shown below:
Note that the range mentioned is 51, which means zero to 50. However, the
range asked in the question is one to 50. Therefore, in the above code, you can
include the range as (1,51).
The output of the above code is as shown:
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Q110. You are given a data set consisting of variables with more
than 30 percent missing values. How will you deal with them?
The following are ways to handle missing data values:
If the data set is large, we can just simply remove the rows with missing data
values. It is the quickest way; we use the rest of the data to predict the values.
For smaller data sets, we can substitute missing values with the mean or
average of the rest of the data using the pandas data frame in python. There
are different ways to do so, such as df.mean(), df.fillna(mean).
Q111. For the given points, how will you calculate the Euclidean
distance in Python?
plot1 = [1,3]
plot2 = [2,5]
The Euclidean distance can be calculated as follows:
euclidean_distance = sqrt( (plot1[0]-plot2[0])**2 + (plot1[1]-plot2[1])**2 )
Q112. What are dimensionality reduction and its benefits?
Dimensionality reduction refers to the process of converting a data set with
vast dimensions into data with fewer dimensions (fields) to convey similar
information concisely.
This reduction helps in compressing data and reducing storage space. It also
reduces computation time as fewer dimensions lead to less computing. It
removes redundant features; for example, there's no point in storing a value in
two different units (meters and inches).
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Q113. How will you calculate eigenvalues and eigenvectors of the
following 3x3 matrix?
-2
-4
2
-2
1
2
4
2
5
The characteristic equation is as shown:
Expanding determinant:
(-2 – λ) [(1-λ) (5-λ)-2x2] + 4[(-2) x (5-λ) -4x2] + 2[(-2) x 2-4(1-λ)] =0
- λ3 + 4λ2 + 27λ – 90 = 0,
λ3 - 4 λ2 -27 λ + 90 = 0
Here we have an algebraic equation built from the eigenvectors.
By hit and trial:
33 – 4 x 32 - 27 x 3 +90 = 0
Hence, (λ - 3) is a factor:
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λ3 - 4 λ2 - 27 λ +90 = (λ – 3) (λ2 – λ – 30)
Eigenvalues are 3,-5,6:
(λ – 3) (λ2 – λ – 30) = (λ – 3) (λ+5) (λ-6),
Calculate eigenvector for λ = 3
For X = 1,
-5 - 4Y + 2Z =0,
-2 - 2Y + 2Z =0
Subtracting the two equations:
3 + 2Y = 0,
Subtracting back into second equation:
Y = -(3/2)
Z = -(1/2)
Similarly, we can calculate the eigenvectors for -5 and 6.
Q114. How should you maintain a deployed model?
The steps to maintain a deployed model are:
Monitor
Constant monitoring of all models is needed to determine their performance
accuracy. When you change something, you want to figure out how your
changes are going to affect things. This needs to be monitored to ensure it's
doing what it's supposed to do.
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Evaluate
Evaluation metrics of the current model are calculated to determine if a new
algorithm is needed.
Compare
The new models are compared to each other to determine which model
performs the best.
Rebuild
The best performing model is re-built on the current state of data.
Q115. What are recommender systems?
A recommender system predicts what a user would rate a specific product
based on their preferences. It can be split into two different areas:
Collaborative filtering
As an example, Last.fm recommends tracks that other users with similar
interests play often. This is also commonly seen on Amazon after making a
purchase; customers may notice the following message accompanied by
product recommendations: "Users who bought this also bought…"
Content-based filtering
As an example: Pandora uses the properties of a song to recommend music
with similar properties. Here, we look at content, instead of looking at who
else is listening to music.
Q116. How do you find RMSE and MSE in a linear regression
model?
RMSE and MSE are two of the most common measures of accuracy for a
linear regression model.
RMSE indicates the Root Mean Square Error.
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MSE indicates the Mean Square Error.
Q117. How can you select k for k-means?
We use the elbow method to select k for k-means clustering. The idea of the
elbow method is to run k-means clustering on the data set where 'k' is the
number of clusters.
Within the sum of squares (WSS), it is defined as the sum of the squared
distance between each member of the cluster and its centroid.
Q118. What is the significance of p-value?
p-value typically ≤ 0.05
This indicates strong evidence against the null hypothesis; so you reject the
null hypothesis.
p-value typically > 0.05
This indicates weak evidence against the null hypothesis, so you accept the
null hypothesis.
p-value at cutoff 0.05
This is considered to be marginal, meaning it could go either way.
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Q119. How can outlier values be treated?
You can drop outliers only if it is a garbage value.
Example: height of an adult = abc ft. This cannot be true, as the height cannot
be a string value. In this case, outliers can be removed.
If the outliers have extreme values, they can be removed. For example, if all
the data points are clustered between zero to 10, but one point lies at 100,
then we can remove this point.
If you cannot drop outliers, you can try the following:
Try a different model. Data detected as outliers by linear models can be fit
by nonlinear models. Therefore, be sure you are choosing the correct
model.
Try normalizing the data. This way, the extreme data points are pulled to a
similar range.
You can use algorithms that are less affected by outliers; an example would
be random forests.
Q120. How can a time-series data be declared as stationery?
It is stationary when the variance and mean of the series are constant with
time.
Here is a visual example:
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In the first graph, the variance is constant with time. Here, X is the time factor
and Y is the variable. The value of Y goes through the same points all the time;
in other words, it is stationary.
In the second graph, the waves get bigger, which means it is non-stationary
and the variance is changing with time.
Q121. How can you calculate accuracy using a confusion matrix?
Consider this confusion matrix:
You can see the values for total data, actual values, and predicted values.
The formula for accuracy is:
Accuracy = (True Positive + True Negative) / Total Observations
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= (262 + 347) / 650
= 609 / 650
= 0.93
As a result, we get an accuracy of 93 percent.
Q122. Write the equation and calculate the precision and recall
rate.
Consider the same confusion matrix used in the previous question.
Precision = (True positive) / (True Positive + False Positive)
= 262 / 277
= 0.94
Recall Rate = (True Positive) / (Total Positive + False Negative)
= 262 / 288
= 0.90
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Q123. 'People who bought this also bought…' recommendations
seen on Amazon are a result of which algorithm?
The recommendation engine is accomplished with collaborative filtering.
Collaborative filtering explains the behavior of other users and their purchase
history in terms of ratings, selection, etc.
The engine makes predictions on what might interest a person based on the
preferences of other users. In this algorithm, item features are unknown.
For example, a sales page shows that a certain number of people buy a new
phone and also buy tempered glass at the same time. Next time, when a
person buys a phone, he or she may see a recommendation to buy tempered
glass as well.
Q124. You are given a dataset on cancer detection. You have built a
classification model and achieved an accuracy of 96 percent. Why
shouldn't you be happy with your model performance? What can
you do about it?
Cancer detection results in imbalanced data. In an imbalanced dataset,
accuracy should not be based as a measure of performance. It is important to
focus on the remaining four percent, which represents the patients who were
wrongly diagnosed. Early diagnosis is crucial when it comes to cancer
detection, and can greatly improve a patient's prognosis.
Hence, to evaluate model performance, we should use Sensitivity (True
Positive Rate), Specificity (True Negative Rate), F measure to determine the
class wise performance of the classifier.
Q125. Which of the following machine learning algorithms can be
used for inputting missing values of both categorical and
continuous variables?
K-means clustering
Linear regression
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K-NN (k-nearest neighbor)
Decision trees
The K nearest neighbor algorithm can be used because it can compute the
nearest neighbor and if it doesn't have a value, it just computes the nearest
neighbor based on all the other features.
When you're dealing with K-means clustering or linear regression, you need to
do that in your pre-processing, otherwise, they'll crash. Decision trees also
have the same problem, although there is some variance.
Q126. Below are the eight actual values of the target variable in the
train file. What is the entropy of the target variable?
[0, 0, 0, 1, 1, 1, 1, 1]
Choose the correct answer.
1. -(5/8 log(5/8) + 3/8 log(3/8))
2. 5/8 log(5/8) + 3/8 log(3/8)
3. 3/8 log(5/8) + 5/8 log(3/8)
4. 5/8 log(3/8) – 3/8 log(5/8)
The target variable, in this case, is 1.
The formula for calculating the entropy is:
Putting p=5 and n=8, we get
Entropy = A = -(5/8 log(5/8) + 3/8 log(3/8))
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Q127. We want to predict the probability of death from heart
disease based on three risk factors: age, gender, and blood
cholesterol level. What is the most appropriate algorithm for this
case?
Choose the correct option:
1. Logistic Regression
2. Linear Regression
3. K-means clustering
4. Apriori algorithm
The most appropriate algorithm for this case is A, logistic regression.
Q128. After studying the behavior of a population, you have
identified four specific individual types that are valuable to your
study. You would like to find all users who are most similar to each
individual type. Which algorithm is most appropriate for this
study?
Choose the correct option:
1. K-means clustering
2. Linear regression
3. Association rules
4. Decision trees
As we are looking for grouping people together specifically by four different
similarities, it indicates the value of k. Therefore, K-means clustering (answer
A) is the most appropriate algorithm for this study.
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Q129. You have run the association rules algorithm on your
dataset, and the two rules {banana, apple} => {grape} and {apple,
orange} => {grape} have been found to be relevant. What else must
be true?
Choose the right answer:
1. {banana, apple, grape, orange} must be a frequent itemset
2. {banana, apple} => {orange} must be a relevant rule
3. {grape} => {banana, apple} must be a relevant rule
4. {grape, apple} must be a frequent itemset
The answer is A: {grape, apple} must be a frequent itemset
Q130. Your organization has a website where visitors randomly
receive one of two coupons. It is also possible that visitors to the
website will not receive a coupon. You have been asked to
determine if offering a coupon to website visitors has any impact
on their purchase decisions. Which analysis method should you
use?
1. One-way ANOVA
2. K-means clustering
3. Association rules
4. Student's t-test
The answer is A: One-way ANOVA
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Additional Data Science Interview Questions on Basic Concepts
131. What are the feature vectors?
A feature vector is an n-dimensional vector of numerical features that
represent an object. In machine learning, feature vectors are used to represent
numeric or symbolic characteristics (called features) of an object in a
mathematical way that's easy to analyze.
132. What are the steps in making a decision tree?
1. Take the entire data set as input.
2. Look for a split that maximizes the separation of the classes. A split is any
test that divides the data into two sets.
3. Apply the split to the input data (divide step).
4. Re-apply steps one and two to the divided data.
5. Stop when you meet any stopping criteria.
6. This step is called pruning. Clean up the tree if you went too far doing
splits.
133. What is root cause analysis?
Root cause analysis was initially developed to analyze industrial accidents but
is now widely used in other areas. It is a problem-solving technique used for
isolating the root causes of faults or problems. A factor is called a root cause if
its deduction from the problem-fault-sequence averts the final undesirable
event from recurring.
134. What is logistic regression?
Logistic regression is also known as the logit model. It is a technique used to
forecast the binary outcome from a linear combination of predictor variables.
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135. What are recommender systems?
Recommender systems are a subclass of information filtering systems that are
meant to predict the preferences or ratings that a user would give to a product.
136. Explain cross-validation.
Cross-validation is a model validation technique for evaluating how the
outcomes of a statistical analysis will generalize to an independent data set. It
is mainly used in backgrounds where the objective is to forecast and one wants
to estimate how accurately a model will accomplish in practice.
The goal of cross-validation is to term a data set to test the model in the
training phase (i.e. validation data set) to limit problems like overfitting and
gain insight into how the model will generalize to an independent data set.
137. What is collaborative filtering?
Most recommender systems use this filtering process to find patterns and
information by collaborating perspectives, numerous data sources, and several
agents.
138. Do gradient descent methods always converge to similar
points?
They do not, because in some cases, they reach a local minima or a local
optima point. You would not reach the global optima point. This is governed
by the data and the starting conditions.
139. What is the goal of A/B Testing?
This is statistical hypothesis testing for randomized experiments with two
variables, A and B. The objective of A/B testing is to detect any changes to a
web page to maximize or increase the outcome of a strategy.
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140. What are the drawbacks of the linear model?
The assumption of linearity of the errors
It can't be used for count outcomes or binary outcomes
There are overfitting problems that it can't solve
141. What is the law of large numbers?
It is a theorem that describes the result of performing the same experiment
very frequently. This theorem forms the basis of frequency-style thinking. It
states that the sample mean, sample variance and sample standard deviation
converge to what they are trying to estimate.
142. What are the confounding variables?
These are extraneous variables in a statistical model that correlates directly or
inversely with both the dependent and the independent variable. The estimate
fails to account for the confounding factor.
143. What is star schema?
It is a traditional database schema with a central table. Satellite tables map
IDs to physical names or descriptions and can be connected to the central fact
table using the ID fields; these tables are known as lookup tables and are
principally useful in real-time applications, as they save a lot of memory.
Sometimes, star schemas involve several layers of summarization to recover
information faster.
144. How regularly must an algorithm be updated?
You will want to update an algorithm when:
You want the model to evolve as data streams through infrastructure
The underlying data source is changing
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There is a case of non-stationarity
145. What are eigenvalue and eigenvector?
Eigenvalues are the directions along which a particular linear transformation
acts by flipping, compressing, or stretching.
Eigenvectors are for understanding linear transformations. In data analysis,
we usually calculate the eigenvectors for a correlation or covariance matrix.
146. Why is resampling done?
Resampling is done in any of these cases:
Estimating the accuracy of sample statistics by using subsets of accessible
data, or drawing randomly with replacement from a set of data points
Substituting labels on data points when performing significance tests
Validating models by using random subsets (bootstrapping, cross-
validation)
147. What is selection bias?
Selection bias, in general, is a problematic situation in which error is
introduced due to a non-random population sample.
148. What are the types of biases that can occur during sampling?
1. Selection bias
2. Undercoverage bias
3. Survivorship bias
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149. What is survivorship bias?
Survivorship bias is the logical error of focusing on aspects that support
surviving a process and casually overlooking those that did not because of
their lack of prominence. This can lead to wrong conclusions in numerous
ways.
150. How do you work towards a random forest?
The underlying principle of this technique is that several weak learners
combine to provide a strong learner. The steps involved are:
1. Build several decision trees on bootstrapped training samples of data
2. On each tree, each time a split is considered, a random sample of mm
predictors is chosen as split candidates out of all pp predictors
3. Rule of thumb: At each split m=p√m=p
4. Predictions: At the majority rule
I hope this set of Data Science Interview Questions and Answers will help you
in preparing for your interviews. All the best!!
Credit: edureka! & simplilearn
